Robust Terrain Identification and Path Planning for Autonomous Ground Vehicles in Unstructured Environments

Project started in 2018 and is ongoing.

Autonomous navigation by robots in challenging terrain and varying environmental conditions remains a difficult and open research problem. The goal of this project is a robust motion planning system for ground robots. Most autonomous robot systems are bespoke; hand-tuned and optimized for a specific platform and set of operating conditions. By robust, we mean to say that the system can be easily adapted to perform on a variety of platforms and over a range of conditions. Implicit here is that the autonomous navigation system must apply a certain level of self-supervised tuning of system parameters that react to changes in platform (i.e. loss of functionality) or environmental conditions (i.e. change in terrain due to weather).

The aim of this research project is two-fold:

1. The development of a robust motion planner capable of plotting a feasible trajectory through challenging non-planar terrain between arbitrary start and goal positions in a predefined map.
2. Assuming an autonomous robot system equipped with a camera and LiDAR system what is the effect on performance (either in terms of completion rate or overall path length) of a reduction in sensor resolution. Accordingly, understanding the minimum resolution required to complete a task (in this case traversing a known trajectory) we also better understand the necessary level of redundancy.